{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import igraph as ig\n",
    "import numpy as np\n",
    "from sklearn.metrics import adjusted_rand_score as ARI\n",
    "from sklearn.metrics import adjusted_mutual_info_score as AMI\n",
    "from sklearn.metrics import normalized_mutual_info_score as NMI\n",
    "import scipy.stats as ss\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "def community_ecg(self, weights=None, ens_size=16, min_weight=0.05):\n",
    "    W = [0]*self.ecount()\n",
    "    ## Ensemble of level-1 Louvain \n",
    "    for i in range(ens_size):\n",
    "        p = np.random.permutation(self.vcount()).tolist()\n",
    "        g = self.permute_vertices(p)\n",
    "        l = g.community_multilevel(weights=weights, return_levels=True)[0].membership\n",
    "        b = [l[p[x.tuple[0]]]==l[p[x.tuple[1]]] for x in self.es]\n",
    "        W = [W[i]+b[i] for i in range(len(W))]\n",
    "    W = [min_weight + (1-min_weight)*W[i]/ens_size for i in range(len(W))]\n",
    "    part = self.community_multilevel(weights=W)\n",
    "    part._modularity_params['weights'] = weights\n",
    "    part.recalculate_modularity()\n",
    "    ## Force min_weight outside 2-core\n",
    "    core = self.shell_index()\n",
    "    ecore = [min(core[x.tuple[0]],core[x.tuple[1]]) for x in self.es]\n",
    "    part.W = [W[i] if ecore[i]>1 else min_weight for i in range(len(ecore))]\n",
    "    part.CSI = 1-2*np.sum([min(1-i,i) for i in part.W])/len(part.W)\n",
    "    return part\n",
    "\n",
    "ig.Graph.community_ecg = community_ecg\n",
    "\n",
    "def readGraph(fn, directed=False):\n",
    "    g = ig.Graph.Read_Ncol(fn+'.edgelist',directed=directed)\n",
    "    c = np.loadtxt(fn+'.community',dtype='uint8')\n",
    "    node_base = min([int(x['name']) for x in g.vs]) ## graphs have 1-based or 0-based nodes \n",
    "    comm_base = min(c) ## same for communities\n",
    "    comm = [c[int(x['name'])-node_base]-comm_base for x in g.vs]\n",
    "    g.vs['community'] = comm\n",
    "    g.vs['shape'] = 'circle'\n",
    "    pal = ig.RainbowPalette(n=max(comm)+1)\n",
    "    g.vs['color'] = [pal.get(int(i)) for i in comm]\n",
    "    g.vs['size'] = 10\n",
    "    g.es['width'] = 1\n",
    "    return g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def edgeLabels(g, gcomm):\n",
    "    x = [(gcomm[x.tuple[0]]==gcomm[x.tuple[1]]) for x in g.es]\n",
    "    return x\n",
    "def AGRI(g, u, v):\n",
    "    bu = edgeLabels(g, u)\n",
    "    bv = edgeLabels(g, v)\n",
    "    su = np.sum(bu)\n",
    "    sv = np.sum(bv)\n",
    "    suv = np.sum(np.array(bu)*np.array(bv))\n",
    "    m = len(bu)\n",
    "    return((suv-su*sv/m) / (0.5*(su+sv)- su*sv/m))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare ECG, ML, IM over large LFR Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## read large noisy LFR graph (with mu=.48, n=8916) \n",
    "g = readGraph('Data/LFR8916/lfr8916')\n",
    "g = g.simplify()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ec = g.community_ecg()\n",
    "im = g.community_infomap()\n",
    "ml = g.community_multilevel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjusted RAND Index\n",
      "ML: 0.8531126539756347\n",
      "ECG: 0.9764555080904431\n",
      "IM: 0.905613384995525\n"
     ]
    }
   ],
   "source": [
    "print('Adjusted RAND Index')\n",
    "print('ML:',ARI(g.vs['community'],ml.membership))\n",
    "print('ECG:',ARI(g.vs['community'],ec.membership))\n",
    "print('IM:',ARI(g.vs['community'],im.membership))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjusted Graph-aware RAND Index\n",
      "ML: 0.9748848309501823\n",
      "ECG: 0.9931338817530508\n",
      "IM: 0.9648339205327461\n"
     ]
    }
   ],
   "source": [
    "print('Adjusted Graph-aware RAND Index')\n",
    "print('ML:',AGRI(g,g.vs['community'],ml.membership))\n",
    "print('ECG:',AGRI(g,g.vs['community'],ec.membership))\n",
    "print('IM:',AGRI(g,g.vs['community'],im.membership))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of communities: 70\n",
      "with ML: 34\n",
      "with ECG: 61\n",
      "with IM: 219\n"
     ]
    }
   ],
   "source": [
    "## Number of clusters found\n",
    "print('number of communities:',max(g.vs['community'])+1)\n",
    "print('with ML:',max(ml.membership)+1)\n",
    "print('with ECG:',max(ec.membership)+1)\n",
    "print('with IM:',max(im.membership)+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "noise (mu): 0.47486422973565556\n"
     ]
    }
   ],
   "source": [
    "## Real vs empirical 'noise' levels\n",
    "\n",
    "m = g.vs['community'] \n",
    "m = ml.membership\n",
    "#m = ec.membership\n",
    "#m = im.membership\n",
    "\n",
    "## compute 'mu' (prop. of external edges)\n",
    "cnt = 0\n",
    "for e in g.es:\n",
    "    if m[e.tuple[0]] != m[e.tuple[1]]:\n",
    "        cnt+=1\n",
    "print('noise (mu):',cnt/g.ecount())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Re-visit the ring of clique problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "## ring of cliques igraph with n cliques of size m with e edges between contiguous cliques\n",
    "def ringOfCliques(n=24, m=5, e=1):\n",
    "    size = n*m\n",
    "    g = ig.Graph()\n",
    "    for i in range(size):\n",
    "        g.add_vertex(str(i))\n",
    "    ## ring of cliques\n",
    "    for i in range(0, size, m):\n",
    "        ## cliques    \n",
    "        for j in range(i,i+m-1,1):\n",
    "            for k in range(j+1,i+m,1):\n",
    "                g.add_edge(str(j),str(k),type='intra')\n",
    "        ## ring\n",
    "        if i>0:\n",
    "            ## all pairs (i,i+1..i+m-1) and (i-m,i-m+1..i-m+m-1)\n",
    "            a = np.arange(i,i+m,1)\n",
    "            b = np.arange(i-m,i,1)\n",
    "        else:\n",
    "            a = np.arange(0,m,1)\n",
    "            b = np.arange(size-m,size,1)\n",
    "        ## all 2-ples: pick e\n",
    "        l = list(itertools.product(a,b))\n",
    "        arr = np.empty(len(l), dtype='O')\n",
    "        arr[:] = l\n",
    "        x = np.random.choice(arr,size=e,replace=False)\n",
    "        for j in x:\n",
    "            g.add_edge(str(j[0]),str(j[1]),type='extra')\n",
    "    return(g)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "## number of communities: ML vs ECG vs IM\n",
    "## n 5-cliques for 4 <= n <= 48\n",
    "## number of linking edges from 1 to 5\n",
    "N = np.arange(4,49,4) ## number of cliques\n",
    "ML=[]\n",
    "IM=[]\n",
    "EC=[]\n",
    "REP=10 ## take average over several repeats\n",
    "for e in range(5): ## number of linking edges\n",
    "    ML.append([])\n",
    "    IM.append([])\n",
    "    EC.append([])\n",
    "    for n in N:\n",
    "        ml=0\n",
    "        im=0\n",
    "        ec=0\n",
    "        for ctr in range(REP):\n",
    "            g = ringOfCliques(n=n, m=5, e=e+1)\n",
    "            ml = ml + max(g.community_multilevel().membership)+1\n",
    "            im = im + max(g.community_infomap().membership)+1\n",
    "            ecg = g.community_ecg(ens_size=32)\n",
    "            ec = ec + max(ecg.membership)+1\n",
    "        ML[e].append(ml/REP)\n",
    "        EC[e].append(ec/REP)\n",
    "        IM[e].append(im/REP)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot the results\n",
    "fig = plt.figure(1, figsize=(16,4))\n",
    "with sns.axes_style('whitegrid'):\n",
    "    for e in range(5):\n",
    "        plt.subplot(1,5,e+1)\n",
    "        plt.plot(N,EC[e],'-', c='b',label='ECG')\n",
    "        plt.plot(N,ML[e],'--', c='g',label='ML')\n",
    "        plt.plot(N,IM[e],':', c='r',label='IM')\n",
    "        plt.xlabel('Number of 5-cliques (n)', fontsize=12)\n",
    "        if e==0:\n",
    "            plt.ylabel('Number of communities found', fontsize=12)\n",
    "        plt.legend(fontsize=12)\n",
    "        plt.title(str(e+1)+' linking edge(s)', fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "## fix n=4 cliques; \n",
    "## look at ECG weights for edges internal/external to the cliques\n",
    "## consider 1 to 15 edges between the cliques\n",
    "## recall: 5-clique has 10 internal edges\n",
    "\n",
    "n = 4\n",
    "EXT = []  ## mean\n",
    "INT = []\n",
    "sEXT = [] ## stdv\n",
    "sINT = []\n",
    "\n",
    "REP=30\n",
    "for xe in range(15):\n",
    "    intern=[]\n",
    "    extern=[]\n",
    "    for ctr in range(REP):\n",
    "        g = ringOfCliques(n=n, m=5, e=xe+1)\n",
    "        ecg = g.community_ecg(ens_size=32)\n",
    "        g.es['weight'] = ecg.W\n",
    "        for e in g.es:\n",
    "            if e['type'] == 'intra':\n",
    "                intern.append(e['weight'])\n",
    "            else:\n",
    "                extern.append(e['weight'])\n",
    "\n",
    "    INT.append(np.mean(intern))\n",
    "    EXT.append(np.mean(extern))    \n",
    "    sINT.append(np.std(intern))\n",
    "    sEXT.append(np.std(extern))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot the results\n",
    "xe = np.arange(1,16,1)\n",
    "fig = plt.figure(1, figsize=(7,5))\n",
    "with sns.axes_style('whitegrid'):\n",
    "\n",
    "    plt.plot(xe,INT,'-', c='b',label='Internal Edges')\n",
    "    plt.fill_between(xe, [INT[i]-sINT[i] for i in range(len(INT))], \n",
    "                         [INT[i]+sINT[i] for i in range(len(INT))],\n",
    "                          alpha=.1, facecolor='black')\n",
    "    plt.plot(xe,EXT,'--', c='g',label='Linking Edges')\n",
    "    plt.fill_between(xe, [EXT[i]-sEXT[i] for i in range(len(EXT))], \n",
    "                         [EXT[i]+sEXT[i] for i in range(len(EXT))],\n",
    "                          alpha=.1, facecolor='black')\n",
    "    plt.xlabel('Number of linking edges between cliques', fontsize=12)\n",
    "    plt.ylabel('ECG Weight', fontsize=12)\n",
    "    plt.legend(fontsize=12)\n",
    "    plt.title('Ring of 4 cliques of size 5', fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"400pt\" height=\"400pt\" viewBox=\"0 0 400 400\" version=\"1.1\">\n",
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       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<igraph.drawing.Plot at 0x7fa9791ba4d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "image/svg+xml": {
       "isolated": true
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## with 'xe' edges between cliques, plot thick edges when ECG weight > Thresh \n",
    "xe  = 15\n",
    "Thresh = .8\n",
    "\n",
    "##\n",
    "size  = 5\n",
    "g = ringOfCliques(n=4, m=size, e=xe)\n",
    "ecg = g.community_ecg(ens_size=32)\n",
    "g.es['weight'] = ecg.W\n",
    "for e in g.es:\n",
    "    if e['type'] == 'intra':\n",
    "        intern.append(e['weight'])\n",
    "    else:\n",
    "        extern.append(e['weight'])\n",
    "cl = np.repeat('blue',size).tolist()+np.repeat('red',size).tolist()+np.repeat('green',size).tolist()+np.repeat('cyan',size).tolist()\n",
    "g.vs['color'] = cl\n",
    "g.es['color'] = 'grey'\n",
    "g.es['width'] = 1\n",
    "for e in g.es:\n",
    "    if e['weight'] >= Thresh:\n",
    "        e['color'] = 'black'\n",
    "        e['width'] = 2\n",
    "\n",
    "ly = g.layout_kamada_kawai()\n",
    "ig.plot(g, target='roc3.pdf', layout=ly, vertex_size=12, bbox=(0,0,400,400))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Looking at the ECG weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CSI: 0.922124335106383\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/francois/anaconda3/envs/social-network-problemshop/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Try various graphs:\n",
    "g = readGraph('Data/LFR15/lfr15')\n",
    "g = readGraph('Data/LFR35/lfr35')\n",
    "#g = readGraph('Data/LFR55/lfr55')\n",
    "#g = ig.Graph.Erdos_Renyi(n=100, m=500)\n",
    "\n",
    "## Print CSI and plot weight distribution\n",
    "ec = g.community_ecg()\n",
    "print('CSI:',ec.CSI)\n",
    "sns.violinplot(ec.W, inner=None)\n",
    "plt.xlabel('ECG weight');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seed expansion with ECG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = readGraph('Data/LFR35/lfr35')\n",
    "g.vs['size'] = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<path style=\"fill-rule:nonzero;fill:rgb(0%,100%,66.666667%);fill-opacity:1;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;\" d=\"M 369.789062 134.945312 C 369.789062 141.609375 359.789062 141.609375 359.789062 134.945312 C 359.789062 128.277344 369.789062 128.277344 369.789062 134.945312 \"/>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<igraph.drawing.Plot at 0x7fa9791fbf90>"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "image/svg+xml": {
       "isolated": true
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v = 3\n",
    "Vertex = g.vs[v]['name']\n",
    "g.vs[v]['size'] = 20\n",
    "## ego-net\n",
    "sg = g.induced_subgraph(g.neighborhood(v, order=1))\n",
    "ig.plot(sg, bbox=(0,0,400,300))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "<path style=\"fill-rule:nonzero;fill:rgb(0%,100%,66.666667%);fill-opacity:1;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;\" d=\"M 191.15625 141.175781 C 191.15625 147.84375 181.15625 147.84375 181.15625 141.175781 C 181.15625 134.511719 191.15625 134.511719 191.15625 141.175781 \"/>\n",
       "<path style=\"fill-rule:nonzero;fill:rgb(0%,100%,66.666667%);fill-opacity:1;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;\" d=\"M 207.300781 216.808594 C 207.300781 223.476562 197.300781 223.476562 197.300781 216.808594 C 197.300781 210.144531 207.300781 210.144531 207.300781 216.808594 \"/>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<igraph.drawing.Plot at 0x7fa9791fb6d0>"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "image/svg+xml": {
       "isolated": true
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 2-hops\n",
    "sg = g.induced_subgraph(g.neighborhood(v, order=2))\n",
    "ig.plot(sg, bbox=(0,0,400,300))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<igraph.drawing.Plot at 0x7fa9794ad7d0>"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "image/svg+xml": {
       "isolated": true
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     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Now run ECG and record edge weights\n",
    "ec = g.community_ecg()\n",
    "g.es['w'] = ec.W\n",
    "\n",
    "## only show edges with high weight\n",
    "Thresh = .7\n",
    "g.es['width'] = 0\n",
    "for e in g.es:\n",
    "    if e['w'] > Thresh:\n",
    "        e['width'] = 1\n",
    "\n",
    "sg = g.induced_subgraph(g.neighborhood(v, order=2))\n",
    "ig.plot(sg, bbox=(0,0,400,300))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "<path style=\"fill-rule:nonzero;fill:rgb(66.666667%,100%,0%);fill-opacity:1;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;\" d=\"M 59.589844 20 C 59.589844 26.667969 49.589844 26.667969 49.589844 20 C 49.589844 13.332031 59.589844 13.332031 59.589844 20 \"/>\n",
       "<path style=\"fill-rule:nonzero;fill:rgb(66.666667%,100%,0%);fill-opacity:1;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;\" d=\"M 174.972656 23.746094 C 174.972656 30.410156 164.972656 30.410156 164.972656 23.746094 C 164.972656 17.078125 174.972656 17.078125 174.972656 23.746094 \"/>\n",
       "<path style=\"fill-rule:nonzero;fill:rgb(66.666667%,100%,0%);fill-opacity:1;stroke-width:1;stroke-linecap:butt;stroke-linejoin:miter;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;\" d=\"M 34.699219 79.410156 C 34.699219 86.078125 24.699219 86.078125 24.699219 79.410156 C 24.699219 72.746094 34.699219 72.746094 34.699219 79.410156 \"/>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<igraph.drawing.Plot at 0x7fa9796a7950>"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "image/svg+xml": {
       "isolated": true
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## keep only edges above threshold and connected component with seed\n",
    "ed = [e for e in sg.es if e['w'] < Thresh]\n",
    "sg.delete_edges(ed)\n",
    "\n",
    "## keep connected component with vertex v\n",
    "sg.vs['cc'] = sg.clusters().membership\n",
    "v = sg.vs.find(name = Vertex)\n",
    "cc = v['cc']\n",
    "vd = [x for x in sg.vs if x['cc'] != cc]\n",
    "sg.delete_vertices(vd)\n",
    "\n",
    "## Plot\n",
    "ig.plot(sg, bbox=(0,0,300,200))"
   ]
  }
 ],
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